Set Intersection Size At Least Two
A hard-tier problem at 45% community acceptance, tagged with Array, Greedy, Sorting. Reported in interviews at DP world and 0 others.
Set Intersection Size At Least Two is a hard greedy problem that trips up candidates who default to brute force. You're given a collection of sets and need to find the minimum size of a new set that intersects each input set in at least two elements. DP World and other companies ask this when they want to see if you can recognize when greedy beats exponential search. Most candidates waste time on backtracking or dynamic programming when sorting plus greedy selection solves it cleanly. At 45% acceptance, it's hard enough to derail prep if you haven't seen the pattern. If this hits your live assessment and you blank on the trick, StealthCoder solves it in seconds, invisible to the proctor.
Companies that ask "Set Intersection Size At Least Two"
Set Intersection Size At Least Two is the kind of problem that decides whether you pass. StealthCoder reads the problem on screen and surfaces a working solution in under 2 seconds. Invisible to screen share. The proctor sees nothing. Built by an engineer at a top-10 tech company who can solve these problems cold but didn't want to trust himself in a 90-minute screen share.
Get StealthCoderThe core insight is that sorting the sets by their endpoints forces a greedy choice: pick elements that satisfy as many sets as possible, starting from the most constrained. If you sort by size or try to enumerate all intersections, you explode combinatorially. The greedy move is to always pick the two largest elements from the rightmost sets, because they're most likely to intersect future sets too. Common pitfall: treating this like a standard set cover problem and reaching for backtracking. Another trap is not realizing that once you pick two elements for a set, you don't need to revisit it. The algorithm requires careful bookkeeping of which sets you've already satisfied, and why greedy placement of two elements from the 'smallest' sets (by upper bound) guarantees optimality. When you hit this live and the greedy structure isn't obvious, StealthCoder gives you the working solution instantly.
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Set Intersection Size At Least Two recycles across companies for a reason. It's hard-tier, and most candidates blank under the timer. StealthCoder is the hedge: an AI overlay invisible during screen share. It reads the problem and surfaces a working solution in under 2 seconds. Built by an engineer at a top-10 tech company who can solve these problems cold but didn't want to trust himself in a 90-minute screen share. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Set Intersection Size At Least Two interview FAQ
Is this actually asking for a subset of size k, or the minimum size?+
You must return the minimum size of a set that intersects every input set in at least two elements. The problem is optimization, not decision. If you return 3 when the answer is 2, you're wrong. Greedy is the only way to avoid exponential branches.
Does sorting really solve this, or is it just a heuristic?+
Sorting by upper bound is mathematically sound, not a heuristic. Greedy selection from constrained sets first guarantees you don't waste picks. Proof relies on exchange argument: any non-greedy solution can be improved by swapping in greedy picks.
What's the trap most people fall into?+
Trying to enumerate all possible pairs or using backtracking with memoization. The problem looks like set cover, so candidates reach for DP or branch-and-bound. In reality, greedy on sorted sets is polynomial and optimal. Overthinking costs you the acceptance.
How does this relate to sorting and greedy as topics?+
Sorting constrains the search space so greedy has a clear path. Without sorting, greedy is blind and fails. This problem teaches why greedy isn't just picking locally; you must sort first to make greedy globally optimal.
Is 45% acceptance because it's conceptually hard or because implementation is tricky?+
Both. The greedy insight is non-obvious, and implementation details matter: tracking which sets are satisfied, handling ties in sort order, and ensuring your two picks actually cover all constraints. Code bugs kill half the submissions.
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